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Author | Title | Accn# | Year | Item Type | Claims |
| 21 |
Connor, Patrick L.S |
Inclusive b Jet Production in Proton-Proton Collisions |
I09411 |
2019 |
eBook |
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| 22 |
Keck, Thomas |
Machine Learning at the Belle II Experiment |
I09374 |
2018 |
eBook |
|
| 23 |
Shmueli, Erez |
3rd International Winter School and Conference on Network Science |
I09354 |
2017 |
eBook |
|
| 24 |
Reagan, James R |
Management 4.0 |
I09324 |
2020 |
eBook |
|
| 25 |
Golosovsky, Michael |
Citation Analysis and Dynamics of Citation Networks |
I09186 |
2019 |
eBook |
|
| 26 |
Ghanbarnejad, Fakhteh |
Dynamics On and Of Complex Networks III |
I09167 |
2019 |
eBook |
|
| 27 |
Berea, Anamaria |
Emergence of Communication in Socio-Biological Networks |
I09139 |
2018 |
eBook |
|
| 28 |
Zhao, Haitao |
Feature Learning and Understanding |
I09105 |
2020 |
eBook |
|
| 29 |
Ryabova, Galina O |
Mathematical Modelling of Meteoroid Streams |
I09014 |
2020 |
eBook |
|
| 30 |
Pendrill, Leslie |
Quality Assured Measurement |
I08785 |
2019 |
eBook |
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22.
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| Title | Machine Learning at the Belle II Experiment : The Full Event Interpretation and Its Validation on Belle Data |
| Author(s) | Keck, Thomas |
| Publication | Cham, Springer International Publishing, 2018. |
| Description | XI, 174 p. 84 illus., 16 illus. in color : online resource |
| Abstract Note | This book explores how machine learning can be used to improve the efficiency of expensive fundamental science experiments. The first part introduces the Belle and Belle II experiments, providing a detailed description of the Belle to Belle II data conversion tool, currently used by many analysts. The second part covers machine learning in high-energy physics, discussing the Belle II machine learning infrastructure and selected algorithms in detail. Furthermore, it examines several machine learning techniques that can be used to control and reduce systematic uncertainties. The third part investigates the important exclusive B tagging technique, unique to physics experiments operating at the ?? resonances, and studies in-depth the novel Full Event Interpretation algorithm, which doubles the maximum tag-side efficiency of its predecessor. The fourth part presents a complete measurement of the branching fraction of the rare leptonic B decay ???B???tau nu???, which is used to validate the algorithms discussed in previous parts |
| ISBN,Price | 9783319982496 |
| Keyword(s) | 1. ARTIFICIAL INTELLIGENCE
2. Data-driven Science, Modeling and Theory Building
3. EBOOK
4. EBOOK - SPRINGER
5. ECONOPHYSICS
6. Elementary particles (Physics)
7. Elementary Particles, Quantum Field Theory
8. Measurement Science and Instrumentation
9. Measurement??????
10. PHYSICAL MEASUREMENTS
11. QUANTUM FIELD THEORY
12. Sociophysics
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09374 |
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On Shelf |
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23.
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| Title | 3rd International Winter School and Conference on Network Science : NetSci-X 2017 |
| Author(s) | Shmueli, Erez;Barzel, Baruch;Puzis, Rami |
| Publication | Cham, Springer International Publishing, 2017. |
| Description | VI, 130 p. 32 illus., 17 illus. in color : online resource |
| Abstract Note | This book contains original research chapters related to the interdisciplinary field of complex networks spanning biological and environmental networks, social, technological, and economic networks. Many natural phenomena can be modeled as networks where nodes are the primitive compounds and links represent their interactions, similarities, or distances of sorts. Complex networks have an enormous impact on research in various fields like biology, social sciences, engineering, and cyber-security to name a few. The topology of a network often encompasses important information on the functionality and dynamics of the system or the phenomenon it represents. Network science is an emerging interdisciplinary discipline that provides tools and insights to researchers in a variety of domains. NetSci-X is the central winter conference within the field and brings together leading researchers and innovators to connect, meet, and establish interdisciplinary channels for collaboration. It is the largest and best known event in the area of network science. This text demonstrates how ideas formulated by authors with different backgrounds are transformed into models, methods, and algorithms that are used to study complex systems across different domains and will appeal to researchers and students within in the field. |
| ISBN,Price | 9783319554716 |
| Keyword(s) | 1. Applications of Graph Theory and Complex Networks
2. BIOINFORMATICS
3. Computational Biology/Bioinformatics
4. Computational Social Sciences
5. COMPUTER SIMULATION
6. Data-driven Science, Modeling and Theory Building
7. EBOOK
8. EBOOK - SPRINGER
9. ECONOPHYSICS
10. PHYSICS
11. Simulation and Modeling
12. Social sciences???Computer programs
13. Social sciences???Data processing
14. Sociophysics
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09354 |
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On Shelf |
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24.
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| Title | Management 4.0 : Cases and Methods for the 4th Industrial Revolution |
| Author(s) | Reagan, James R;Singh, Madhusudan |
| Publication | Singapore, Springer Singapore, 2020. |
| Description | XVII, 175 p. 61 illus. in color : online resource |
| Abstract Note | This book provides a comprehensive review of industry 4.0 and its applications, discussing the history of industry evaluation, including industry 1.0, 2.0, 3.0 and 4.0, and the future structure of industry evaluation. It also examines the effects and impact of various technologies in industry and presents new interdisciplinary business models based on advanced technologies with the help of use cases. Lastly, it highlights the benefits of technological implementation in industry using examples of real-world applications, providing a robust and reliable technological conceptual framework and roadmap for decision-makers in all areas of industry involved transformation. |
| ISBN,Price | 9789811567513 |
| Keyword(s) | 1. AUTOMATION
2. COMPUTER ENGINEERING
3. Cyber-physical systems, IoT
4. Data-driven Science, Modeling and Theory Building
5. EBOOK
6. EBOOK - SPRINGER
7. ECONOPHYSICS
8. Embedded computer systems
9. INDUSTRIAL MANAGEMENT
10. Innovation/Technology Management
11. Internet of things
12. MANAGEMENT
13. ROBOTICS
14. Robotics and Automation
15. Sociophysics
16. Statistics for Business, Management, Economics, Finance, Insurance
17. Statistics??
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09324 |
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On Shelf |
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25.
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| Title | Citation Analysis and Dynamics of Citation Networks |
| Author(s) | Golosovsky, Michael |
| Publication | Cham, Springer International Publishing, 2019. |
| Description | XIV, 121 p. 53 illus., 52 illus. in color : online resource |
| Abstract Note | This book deals with the science of science by applying network science methods to citation networks and uniquely presents a physics-inspired model of citation dynamics. This stochastic model of citation dynamics is based on a well-known copying or recursive search mechanism. The measurements covered in this text yield parameters of the model and reveal that citation dynamics of scientific papers is not linear, as was previously assumed. This nonlinearity has far-reaching consequences including non-stationary citation distributions, diverging citation trajectories of similar papers, and runaways or "immortal papers" with an infinite citation lifespan. The author shows us that nonlinear stochastic models of citation dynamics can be the basis for a quantitative probabilistic prediction of citation dynamics of individual papers and of the overall journal impact factor. This book appeals to students and researchers from differing subject areas working in network science and bibliometrics |
| ISBN,Price | 9783030281694 |
| Keyword(s) | 1. Big data
2. BIG DATA ANALYTICS
3. COMPLEX SYSTEMS
4. Data-driven Science, Modeling and Theory Building
5. EBOOK
6. EBOOK - SPRINGER
7. ECONOPHYSICS
8. Sociophysics
9. SYSTEM THEORY
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09186 |
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On Shelf |
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26.
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| Title | Dynamics On and Of Complex Networks III : Machine Learning and Statistical Physics Approaches |
| Author(s) | Ghanbarnejad, Fakhteh;Saha Roy, Rishiraj;Karimi, Fariba;Delvenne, Jean-Charles;Mitra, Bivas |
| Publication | Cham, Springer International Publishing, 2019. |
| Description | X, 244 p. 76 illus., 68 illus. in color : online resource |
| Abstract Note | This book bridges the gap between advances in the communities of computer science and physics--namely machine learning and statistical physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning. Examples of such topics are: predicting missing links, higher-order generative modeling of networks, inferring network structure by tracking the evolution and dynamics of digital traces, recommender systems, and diffusion processes. The book contains extended versions of high-quality submissions received at the workshop, Dynamics On and Of Complex Networks (doocn.org), together with new invited contributions. The chapters will benefit a diverse community of researchers. The book is suitable for graduate students, postdoctoral researchers and professors of various disciplines including sociology, physics, mathematics, and computer science |
| ISBN,Price | 9783030146832 |
| Keyword(s) | 1. COMPLEX SYSTEMS
2. COMPLEXITY
3. COMPUTATIONAL COMPLEXITY
4. Computational Social Sciences
5. Data-driven Science, Modeling and Theory Building
6. EBOOK
7. EBOOK - SPRINGER
8. ECONOPHYSICS
9. Social sciences???Computer programs
10. Social sciences???Data processing
11. Sociophysics
12. SYSTEM THEORY
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09167 |
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On Shelf |
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27.
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| Title | Emergence of Communication in Socio-Biological Networks |
| Author(s) | Berea, Anamaria |
| Publication | Cham, Springer International Publishing, 2018. |
| Description | VIII, 88 p : online resource |
| Abstract Note | This book integrates current advances in biology, economics of information and linguistics research through applications using agent-based modeling and social network analysis to develop scenarios of communication and language emergence in the social aspects of biological communications.??The book presents a model of communication emergence that can be applied both to human and non-human living organism networks. The model is based on economic concepts and individual behavior fundamental for the study of trust and reputation networks in social science, particularly in economics; it is also based on the theory of the emergence of norms and historical path dependence that has been influential in institutional economics. Also included are mathematical models and code for agent-based models to explore various scenarios of language evolution, as well as a computer application that explores language and communication in biological versus social organisms, and the emergence of various meanings and grammars in human networks. Emergence of Communication in Socio-Biological Networks??offers both a completely novel approach to communication emergence and language evolution and provides a path for the reader to explore various scenarios of language and communication that are not constrained to the human networks alone. By illustrating how computational social science and the complex systems approach can incorporate multiple disciplines and offer an integrated theory-model approach to the evolution of language, the book will be of interest to researchers working with computational linguistics, mathematical linguistics, and complex systems |
| ISBN,Price | 9783319645650 |
| Keyword(s) | 1. Biological systems
2. Computational linguistics
3. COMPUTER SIMULATION
4. Data-driven Science, Modeling and Theory Building
5. EBOOK
6. EBOOK - SPRINGER
7. ECONOPHYSICS
8. Simulation and Modeling
9. Sociophysics
10. Systems biology
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09139 |
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On Shelf |
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28.
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| Title | Feature Learning and Understanding : Algorithms and Applications |
| Author(s) | Zhao, Haitao;Lai, Zhihui;Leung, Henry;Zhang, Xianyi |
| Publication | Cham, Springer International Publishing, 2020. |
| Description | XIV, 291 p. 126 illus., 109 illus. in color : online resource |
| Abstract Note | This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence |
| ISBN,Price | 9783030407940 |
| Keyword(s) | 1. Computational Intelligence
2. Data-driven Science, Modeling and Theory Building
3. EBOOK
4. EBOOK - SPRINGER
5. ECONOPHYSICS
6. IMAGE PROCESSING
7. Image Processing and Computer Vision
8. MACHINE LEARNING
9. OPTICAL DATA PROCESSING
10. PATTERN RECOGNITION
11. SIGNAL PROCESSING
12. Signal, Image and Speech Processing
13. Sociophysics
14. Speech processing systems
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09105 |
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On Shelf |
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29.
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| Title | Mathematical Modelling of Meteoroid Streams |
| Author(s) | Ryabova, Galina O |
| Publication | Cham, Springer International Publishing, 2020. |
| Description | VIII, 68 p. 16 illus., 8 illus. in color : online resource |
| Abstract Note | Modern computer power and high-precision observational data have greatly improved the reliability of meteoroid stream models. At present, scientific research calls for two kinds of models: precise ones for individual streams, and statistically averaged ones for Solar System dust distribution models. Thus, there is a wide field of study open to stream modellers. This brief describes step-by-step computer simulations of meteoroid stream formation and evolution. Detailed derivations of relevant formulae are given, along with plenty of helpful, digestible figures explaining the subtleties of the method. Each theoretical section ends with examples aimed to help readers practice and master the material. Most of the examples are based on the Geminid meteoroid stream model, which has been developed by the author in the last 30 years. The book is intended for researchers interested in meteor astronomy and mathematical modelling, and it is also accessible to physics and astrophysics students |
| ISBN,Price | 9783030515102 |
| Keyword(s) | 1. ASTROPHYSICS
2. Astrophysics and Astroparticles
3. Data-driven Science, Modeling and Theory Building
4. EBOOK
5. EBOOK - SPRINGER
6. ECONOPHYSICS
7. MATHEMATICAL PHYSICS
8. Sociophysics
9. THEORETICAL ASTROPHYSICS
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I09014 |
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On Shelf |
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30.
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| Title | Quality Assured Measurement : Unification across Social and Physical Sciences |
| Author(s) | Pendrill, Leslie |
| Publication | Cham, Springer International Publishing, 2019. |
| Description | XIII, 241 p. 98 illus., 46 illus. in color : online resource |
| Abstract Note | This book presents a general and comprehensive framework for the assurance of quality in measurements. Written by a foremost expert in the field, the text reflects an on-going international effort to extend traditional quality assured measurement, rooted in fundamental physics and the SI, to include non-physical areas such as person-centred care and the social sciences more generally. Chapter by chapter, the book follows the measurement quality assurance loop, based on Deming???s work. The author enhances this quality assurance cycle with insights from recent research, including work on the politics and philosophy of metrology, the new SI, quantitative and qualitative scales and entropy, decision risks and uncertainty when addressing human challenges, Man as a Measurement Instrument, and Psychometry and Person-centred care. Quality Assured Measurement: Unification across Social and Physical Sciences provides students and researchers in physics, chemistry, engineering, medicine and the social sciences with practical guidance on designing, implementing and applying a quality-assured measurement while engaging readers in the most novel and expansive areas of contemporary measurement research. |
| ISBN,Price | 9783030286958 |
| Keyword(s) | 1. Data-driven Science, Modeling and Theory Building
2. EBOOK
3. EBOOK - SPRINGER
4. ECONOPHYSICS
5. Industrial safety
6. Measurement Science and Instrumentation
7. Measurement??????
8. PHYSICAL MEASUREMENTS
9. PSYCHOMETRICS
10. QUALITY CONTROL
11. Quality Control, Reliability, Safety and Risk
12. RELIABILITY
13. RESEARCH METHODOLOGY
14. Sociology???Research
15. Sociophysics
|
| Item Type | eBook |
Multi-Media Links
Please Click here for eBook
Circulation Data
| Accession# | |
Call# | Status | Issued To | Return Due On | Physical Location |
| I08785 |
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On Shelf |
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